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\section*{Appendix}
\setcounter{table}{0}
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\subsection*{Robustness tests}
First, Table \ref{table:fulltable1} presents the full results for the main  models for conflict onset. In Table \ref{tab:endogeneity}, we address potential concerns about endogeneity, namely the possibility that urban concentration and onset are both (partly) driven by prior conflict. As we discuss in the main text, our substantive knowledge of the effects of conflict on urban geography leads us to expect that this is not the case, as conflict can have both positive or negative effects on concentration depending on how and where conflicts are fought within the country. While there is no truly satisfactory way to resolve this issue, we address this here in two ways. First, we show in Figure A1 the distribution of annual change in concentration during peace years and conflict years, as well as results from a t-test. This shows that while episodes of accelerated concentration during conflict do occur, they are no more likely than in times of peace, and, if anything, urban concentration is slightly more likely to decelerate or even reverse during conflict, at least within our sample. Nonetheless, in Table \ref{tab:endogeneity}, Model 1 the time since any conflict of any intensity (and its cubic polynomials), in Model 2 a longer lag for the concentration variable, and, in Model 3, we excluded all countries from the analysis once they experience outbreak of any armed conflict. 

Then, in Table \ref{table:robust1}, Model 1, we exclude peace years altogether to ensure our findings are not driven by the inclusion of years of peace between the onset of high intensity civil conflicts. Second, to guard against the possibility that our results are largely determined by one or a few outliers, we re-estimate our models by dropping individual cases and then dropping influential observations. For \ref{table:robust1}, Model 2, we simply used the Stata command \texttt{jackknife}, which drops an individual observation, reruns the model, replaces the dropped observations, excludes the following observation, then re-runs the model again. Once all observations have been omitted, new coefficients and estimates are calculated. For \ref{table:robust1}, Model 3, we calculated the Pregibon's beta for all observations and dropped all potentially high-leverage cases. Pregibon's beta is equivalent to Cook's distance in linear regressions. We followed established convention and classified as high-leverage observations those with Pregibon's beta greater than 1. Our findings are robust to each of these tests. We also report results not dropping Singapore from the analysis--a rich, stable, and totally concentrated state that has, by definition, no hinterland or disadvantaged areas where rebels can organize to challenge the state, therefore falling outside the scope of our theory (\ref{table:singapore}). The results are nonetheless robust to its inclusion.

Additional tables replicate the results of our main analysis on conflict onset controlling for alternative measures of regime type (\ref{table:regimetype}), namely the dichotomous measure of democracy/non-democracy from \citet{CheibubGandhiVreeland:2010}, as well as their six-way typology of regimes.

In Table \ref{table:resources} we also include measures of the availability of oil, gems and drugs, from \citet{Lujala:2010}; oil rents per capita and an indicator for whether oil accounts for over one third of a country's exports, from \citet{Colgan:2015}. To capture states' military capabilities, in Table \ref{table:military}, we control for expenditures and personnel (per capita, logged), as well as indicators for the production of iron and steel and energy consumption (logged), all from the Correlates of War \citep{Singer:1972}. We don't include these latter factors in our main models for a few reasons. First, we doubt there exists a direct correlation between military capabilities and urban concentration. Second, designed for the study of interstate conflict, the COW dataset counts only forces intended for fighting \textit{foreign} actors, thus excluding internal security forces. Counting only those forces would be misleading: while national militaries are often used for internal repression and combating domestic threats, and governments capable of raising large militaries may also be able to maintain large internal security forces, some militaries are either legally prohibited from or unwilling to perform these functions, and actual levels of military mobilization can be negatively related to the size of internal security forces if recruitment is diverted from the latter to the former. Moreover, military personnel and expenditure and the probability of civil war onset are both positively correlated with the incidence of interstate conflict.

We also report in Table \ref{tab:growth} models controlling for (1) the annual change in GDP per capita and (2) the \emph{pace} of urbanization--i.e. the rate of change in the balance between urban and rural populations, computed as a five-year moving average. We operationalize this as the average rate of change in urbanization over the preceding five years. We find no evidence of an effect in either case, and the results for \emph{urban concentration} are robust to the inclusion of both variables.

In addition, we use two alternative measures of concentration. The first is a different cut-off point for major city size in (\ref{table:300kcutoff}), from \citet{wup:2014}. The only model for which our results come very close to losing significance is Model 5, when decade fixed effects are included. The second replaces urban concentration with \textit{Primacy}, a measure of the population size in the largest urban center (\ref{tab:shareofurban}).

Table \ref{tab:institutions} presents the results of models including a variety of controls relating to states' colonial legacies and political institutions. In model 1, we control for whether the country is a former colony. Countries are coded as 1 if they have no colonial history, and 0 if they are former colonies. In model 2, we also control for whether the country is a former \emph{British} colony (1, 0 otherwise)\footnote{The data is from \citet{Miller:2012}.} We do so following \citep{cederman2015}, who note that the British were far more likely to adopt indirect rule, therefore producing more political decentralization both during and after colonial rule.\footnote{Interestingly, we do not find that former British colonies are on average less concentrated than non-British former colonies.} Model 3 subsets the data to include \emph{only} former colonial states. Following \citep{Acemoglu:2001b} we also control (Model 4) for a measure of settler mortality in the former colony, which the authors find to be a useful instrument for the quality of institutions in former colonies, resulting from more extractive colonial practices. These plausibly affect a country's likelihood to experience civil conflict and, potentially, the country's urban geography.\footnote{Using our own data, we managed to replicate their finding, and also found that settler mortality is not only a decent predictor of income levels, but also of levels of urbanization. Crucially, however, it does not predict levels of urban concentration.} Finally, we use data from \citep[12]{grahametal2017} to control for "dispersive power-sharing institutions", which "divide authority among actors in a well-defined pattern (e.g., territorial decentralization)". This measure, a latent variable constructed from a range of indicators of local autonomy, covers only the years 1975-2010.\footnote{As with settler mortality and British colonial legacy, we do not find that dispersive power-sharing institutions predict .} We again find that our results are robust to these new model specifications, despite the substantial loss in sample size across models. 

For illustration purposes, Table \ref{corrgram} displays the pairwise correlations between variables in these models in the first year for which the data is complete (1975).\footnote{Pooling all years would artificially inflate the pairwise correlations. Results for later years are very similar.} 
In Figure \ref{scatterHHurban}, to better illustrate the relationship between urbanization and urban concentration (or lack thereof) and illustrate how the various countries stack up on both measures, we show a scatterplot juxtaposing the two variables in 2010.

We also present robustness tests for the analysis of conflict intensity, including not lagging the independent variable, adding decade, region, country-fixed effects, and random effects for both log-linear (\ref{tab:log-linear}) and negative-binomial (\ref{tab:nbreg} models). Finally, we model both the onset and intensity of conflict simultaneously using a two-stage hurdle model (\ref{tab:hurdle}). Our results are robust to all of these different approaches.

Finally, in Tables \ref{technologiestablecoll} and \ref{technologiestable}, we test mechanisms by comparing urban concentration to the technology of rebellion that characterized it. In Table \ref{technologiestable} we maintain the tripartite typology of \citet{Kalyvas:2010APSR}, showing that states with above-average levels of urban concentration are far more likely to experience (symmetric) conventional or symmetric nonconventional conflicts than irregular (assymetric) wars.  In Table \ref{technologiestablecoll} we collapse the categories of conventional and symmetric nonconventional conflicts and show that urban concentration is associated with conflicts where rebels and governments are more or less equally matched. This difference is statistically significant at \textit{p} < 0.000.

\begin{table}[h]
\scalebox{0.85}{
\caption{Full Results of Main Models, Table 1}
\label{table:fulltable1}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lccccc}					
	& \multicolumn{1}{c}{Bivariate} &	\multicolumn{1}{c}{Full} &	\multicolumn{1}{c}{No Lag} &	\multicolumn{1}{c}{Dec. FEs} &	\multicolumn{1}{c}{Reg. FEs} \\
\cline{1-6}					
	& \multicolumn{1}{c}{(1)\mbox{\ }} &	\multicolumn{1}{c}{(2)\mbox{\ }} &	\multicolumn{1}{c}{(3)\mbox{\ }} &	\multicolumn{1}{c}{(4)\mbox{\ }} &	\multicolumn{1}{c}{(5)} \\
\hline					
Urban Concentration &	1.93$^{***}$ &	2.42$^{***}$ & 3.25$^{***}$	&	2.32$^{***}$ &	2.25$^{***}$ \\
									&	 { (0.69)} &	 { (0.73)} &	 { (0.90)} &	 { (0.73)} &	 { (0.63)} \\
Urbanization (\%) &	& 0.59 &	-0.11 & 	-0.78 &	0.31 \\
						&	&	 { (0.99)} & { (0.77)} &	 (0.76)& (0.75) \\
X-Polity &	&	0.01 &	0.00 &	0.01 &	0.03 \\
				&	&	 { (0.03)} &	 { (0.03)} &	 { (0.03)} &	 { (0.03)} \\
Population size (log) &	&	0.33$^{***}$ &	0.42$^{***}$ &	0.26$^{**}$ &	0.23$^{***}$ \\
				&	&	 { (0.12)} &	 { (0.16)} &	 { (0.12)} &	 { (0.11)} \\
Pop. Aged 0-24 (\%) &	&	0.06$^{***}$ &	0.06$^{***}$ &	0.05$^{**}$ &	0.08$^{***}$ \\
				&	&	 { (0.02)} &	 { (0.02)} &	 { (0.02)} &	 { (0.03)} \\
GDP per capita (log) &	&	0.03 &	-0.06 &	0.09 &	0.02 \\
				&	&	 { (0.18)} &	 { (0.19)} &	 { (0.19)} &	 { (0.08)} \\
Discrimination &	&	0.00$^{*}$ &	0.01$^{*}$ &	0.00$^{*}$ &	0.00 \\
					&	&	 (0.00) &	 (0.01)&(0.00)&	 (0.00) \\
Mountainous terrain (\% log) &	&	0.20$^{**}$ &	0.20$^{**}$ &	0.20$^{**}$ &	0.22$^{**}$ \\
													&	& (0.09) &	 (0.09) &	 (0.09) &	  (0.10) \\
Territory (mil sq km) &	&	0.07 &	0.05 &	0.10 &	0.13$^{*}$ \\
								&	&	 { (0.10)} &	 { (0.11)} &	 { (0.09)} &	 { (0.08)} \\
Peace years (cubic polynomials) &    No &    Yes &    Yes & Yes & Yes \\
Const. &	-4.35$^{***}$ &	-11.25$^{***}$ &	-13.00$^{***}$ &	-10.30$^{***}$ &	-24.98$^{***}$ \\
			&	 { (0.15)} &	 { (2.92)} &	 { (3.03)} &	 { (2.92)} &	 { (3.02)} \\
\hline
			Obs. &	6162 &	5406 &	5676 &	5406 &	5406 \\
$ \chi^2$ statistic & 6.95    &  125.07   &  105.53   &   136.79 &   315.01     \\
Pseudo \(R^{2}\)    &       0.007         &       0.099         &       0.102         &       0.107         &       0.109         \\
\hline\hline	
\multicolumn{4}{l}{\footnotesize Clustered standard errors in parentheses}\\
\multicolumn{4}{l}{\footnotesize \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular*}%					
}
\end{table}



\begin{figure}[h!]
\begin{center}
\caption{Annual Change in Urban Concentration in Times of Peace and Conflict}
\includegraphics[width=6.5in]{conflict_concentration_endogeneity_hist.pdf}
\label{fig:endogeneityhist}
\end{center}
\end{figure}



\begin{table}

\caption{Addressing endogeneity. Using time since conflict of any intensity, 10-year lag, excluding countries from analysis after first outbreak of conflict}
\label{tab:endogeneity}
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{l*{3}{c}}
                    &\multicolumn{1}{c}{Time since any conflict}&\multicolumn{1}{c}{No prior conflict}&\multicolumn{1}{c}{10-year lag}\\
                    \hline
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}\\
\hline

Urban Concentration (5yr lag) &        1.71\sym{***} &        10.08\sym{***}&                     \\
                    &      (0.66)         &      (3.22)         &                     \\
Urban Concentration (10yr lag) &                     &                     &        2.30\sym{***} \\
                    &                     &                     &      (0.76)         \\
Urbanization (\%) &        -0.77         &       -7.56\sym{*}   &       -0.02  \\
                    &      (0.57)         &      (4.04)         &      (0.68)         \\
X-Polity          &       -0.02         &       -0.06        &       0.00  \\
                    &      (0.03)         &      (0.14)         &      (0.03)         \\
Population size (log) &        0.10   &  0.83\sym{*}       & 0.33\sym{**} \\
                    &     (0.07)      & (0.50)        &     (0.13)         \\
\% of pop. age 0-24 &        0.03         &       -0.10\sym{**}&      0.06\sym{***} \\
                    &      (0.02)         &      (0.05)         &      (0.02)         \\
GDP per capita (log) &   0.13     &      0.02    &        0.08        \\
                    &     (0.17)       &     (0.61)    &     (0.17)         \\
Discrimination    &        0.00         &       -0.02         &        0.01\sym{**}         \\
                    &      (0.00)         &      (0.03)         &      (0.00)         \\
Mountainous terrain (\% log) &        0.19\sym{**} &        1.02\sym{*}  &        0.18\sym{**}         \\
                									    &      (0.08)         &      (0.58)         &      (0.08)         \\
Territory (mil sq km) &        0.13\sym{***}&        0.18  &        0.07        \\
                 					   &      (0.04)         &      (0.16)         &      (0.12)         \\
Peace years (cubic polynomials) &    Yes &    Yes &    Yes \\
Constant & -7.17 & -12.00 & -11.85 \\
& (2.27) & ( 10.23) & (2.97) \\
\hline
Observations        &        5406         &        2878         &        5038         \\
\chi^2 statistic          &    145.39           &      100.50               &      120.04               \\
\(R^{2}\) 							     &   0.19                  &    0.24                 &         0.10            \\
\hline\hline
\multicolumn{4}{l}{\footnotesize Clustered standard errors in parentheses}\\
\multicolumn{4}{l}{\footnotesize \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
}

\end{table}

\begin{table}
\scalebox{0.85}{
\caption{Additional Robustness Tests for Onset of Conflict, Excluding Peace Years, Influential Outliers, Jackknife}
\label{table:robust1}

\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lccc}
& \multicolumn{1}{c}{No Peace Yrs.} &\multicolumn{1}{c}{Jackknife} &\multicolumn{1}{c}{Excl. Infl. Outliers} \\
\cline{1-4}
& \multicolumn{1}{c}{(1)\mbox{\ }} &\multicolumn{1}{c}{(2)\mbox{\ }} &\multicolumn{1}{c}{(3)} \\
\hline
Urban Concentration &	3.38$^{***}$ &	2.42$^{*}$ &	2.82$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.73)} &	\raisebox{.7ex}[0pt]{  (1.26)} &	\raisebox{.7ex}[0pt]{  (.72)} \\
Urbanization (\%) &	-.41 &	-.13 &	-.21 \\
&	\raisebox{.7ex}[0pt]{  (.82)} &	\raisebox{.7ex}[0pt]{  (.80)} &	\raisebox{.7ex}[0pt]{  (.70)} \\
X-Polity  &	.008 &	.0008 &	.003 \\
&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
Population size (log) &	.38$^{**}$ &	.33$^{*}$ &	.48$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.15)} &	\raisebox{.7ex}[0pt]{  (.18)} &	\raisebox{.7ex}[0pt]{  (.10)} \\
Pop. Aged 0-24 (\%) &	.06$^{**}$ &	.06$^{**}$ &	.07$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.02)} \\
GDP per capita (log) &	-.03 &	.03 &	.07 \\
&	\raisebox{.7ex}[0pt]{  (.22)} &	\raisebox{.7ex}[0pt]{  (.20)} &	\raisebox{.7ex}[0pt]{  (.19)} \\
Discrimination &	.01$^{**}$ &	.009 &	.009 \\
&	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.006)} &	\raisebox{.7ex}[0pt]{  (.005)} \\
Mountainous terrain (\% log) &	.23$^{**}$ &	.20$^{*}$ &	.21$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.10)} &	\raisebox{.7ex}[0pt]{  (.10)} &	\raisebox{.7ex}[0pt]{  (.09)} \\
Territory (mil sq km) &	.08 &	.08 &	-.09 \\
&	\raisebox{.7ex}[0pt]{  (.12)} &	\raisebox{.7ex}[0pt]{  (.17)} &	\raisebox{.7ex}[0pt]{  (.07)} \\
Constant &	-11.93$^{***}$ &	-11.25$^{***}$ &	-13.58$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (3.32)} &	\raisebox{.7ex}[0pt]{  (3.73)} &	\raisebox{.7ex}[0pt]{  (2.76)} \\
Obs. &	5406 &	5406 &	5403 \\
$ F$ statistic &	&	6.233 &	\\
$ \chi^2$ statistic &	88.65 &	&	120.4 \\
Pseudo \(R^{2}\)&	0.08 &	0.1 &	0.11 \\
\hline\hline
\multicolumn{4}{l}{\footnotesize Clustered standard errors in parentheses.}\multicolumn{4}{l}{* \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\


\end{tabular*}%
}
\end{table}

\begin{table}
\centering
\caption{Additional Robustness Test, Not Dropping Singapore}
\label{table:singapore}
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{l*{1}{c}}
\hline
 
Urban Concentration &        2.17\sym{***} \\
                    &      (0.68)         \\
Urbanization (\%) &        -0.29         \\
                    &      (0.72)         \\
X-Polity          &        0.02         \\
                    &      (0.03)         \\
Population size &        0.32\sym{***} \\
                    &      (0.12)         \\
\% of pop. age 0-24 &        0.06\sym{**}\\
                    &      (0.02)         \\
GDP per capita (log)         &       0.03         \\
                    &      (0.18)         \\
Discrimination    &        0.009\sym{***}\\
                    &      (0.005)         \\
Mountainous terrain (\% log) &        0.21\sym{**}  \\
                    &      (0.09)         \\
Territory (mil sq km) &        0.8         \\
                    &      (0.10)         \\
Constant            &      -8.86\sym{***}\\
                    &      (2.53)         \\
\hline
Observations        &        5449         \\
\(R^{2}\)           &        0.099             \\
\hline\hline
\multicolumn{2}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
}
\end{table}


\begin{table}
\caption{Additional Robustness Tests for Onset of Conflict, Different Measures of Regime Type}
\label{table:regimetype}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lcc}
& Dichotomous Measure &Six-Way Typology \\
\cline{1-3}
& \multicolumn{1}{c}{(1)\mbox{\ }} &\multicolumn{1}{c}{(2)} \\
\hline
Urban Concentration &	2.51$^{***}$ &	2.24$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.67)} &	\raisebox{.7ex}[0pt]{  (.73)} \\
Urbanization (\%) &	-.09 &	-.15 \\
&	\raisebox{.7ex}[0pt]{  (.69)} &	\raisebox{.7ex}[0pt]{  (.72)} \\
Democracy (Dichotomous)  &	-.33 &	\\
&	\raisebox{.7ex}[0pt]{  (.28)} &	\\
Parliamentary &	&	-.02 \\
&	&	\raisebox{.7ex}[0pt]{  (.79)} \\
Semi-parliamentary &	&	-.21 \\
&	&	\raisebox{.7ex}[0pt]{  (.51)} \\
Presidential &	&	.14 \\
&	&	\raisebox{.7ex}[0pt]{  (.42)} \\
Civilian Dictatorship &	&	.55 \\
&	&	\raisebox{.7ex}[0pt]{  (.39)} \\
Military Dictatorship &	&	-1.05 \\
&	&	\raisebox{.7ex}[0pt]{  (.72)} \\
Monarchical Dictatorship && Omitted\\
&&\\
Pop. size (log) &	.38$^{***}$ &	.34$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.09)} &	\raisebox{.7ex}[0pt]{  (.09)} \\
Pop. Aged 0-24 (\%) &	.04$^{*}$ &	.05$^{*}$ \\
&	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
GDP per capita (log) &	.06 &	.11 \\
&	\raisebox{.7ex}[0pt]{  (.18)} &	\raisebox{.7ex}[0pt]{  (.20)} \\
Discrimination &	.009$^{*}$ &	.01$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.004)} \\
Mountainous terrain (\% log) &	.21$^{**}$ &	.25$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.08)} &	\raisebox{.7ex}[0pt]{  (.08)} \\
Territory Size (mil sq km) &	.01 &	.02 \\
&	\raisebox{.7ex}[0pt]{  (.04)} &	\raisebox{.7ex}[0pt]{  (.04)} \\
Peace years (cubic polynomials)  &    Yes &    Yes \\
Constant &	-10.99$^{***}$ &	-11.46$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (2.80)} &	\raisebox{.7ex}[0pt]{  (3.04)} \\
\hline
Obs. &	5508 &	5508 \\
$ F$ statistic &	&	\\
$ \chi^2$ statistic &	126.1 &	162.58 \\
Pseudo \(R^{2}\) &	0.09 &	0.1 \\
\hline\hline
\multicolumn{3}{l}{\footnotesize Standard errors in parentheses. * \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\

\end{tabular*}%
\end{table}




\begin{table}
\caption{Additional Robustness Tests for Onset of Conflict, Natural Resources}
\label{table:resources}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lcc}
& \multicolumn{1}{c}{All Natural Resources} &\multicolumn{1}{c}{Oil Only} \\
\cline{1-3}
& \multicolumn{1}{c}{(1)\mbox{\ }} &\multicolumn{1}{c}{(2)} \\
\hline
Urban Concentration &	2.75$^{***}$ &	2.75$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.87)} &	\raisebox{.7ex}[0pt]{  (.83)} \\
Urbanization &	-.75 &	-.20 \\
&	\raisebox{.7ex}[0pt]{  (.76)} &	\raisebox{.7ex}[0pt]{  (.73)} \\
X-Polity &	.003 &	.01 \\
&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
Population size (log) &	.27$^{*}$ &	.34$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.15)} &	\raisebox{.7ex}[0pt]{  (.13)} \\
Pop. aged 0-24 (\%) &	.05$^{*}$ &	.05$^{*}$ \\
&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
GDP per capita (log) &	.006 &	-.05 \\
&	\raisebox{.7ex}[0pt]{  (.21)} &	\raisebox{.7ex}[0pt]{  (.26)} \\
Discrimination &	.01$^{**}$ &	.008 \\
&	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.006)} \\
Mountainous terrain (\% log) &	.18$^{*}$ &	.21$^{*}$ \\
&	\raisebox{.7ex}[0pt]{  (.10)} &	\raisebox{.7ex}[0pt]{  (.11)} \\
Territory size &	.05 &	.04 \\
&	\raisebox{.7ex}[0pt]{  (.10)} &	\raisebox{.7ex}[0pt]{  (.10)} \\
Coca, cannabis or opium (dummy) &	.12 &	\\
&	\raisebox{.7ex}[0pt]{  (.30)} &	\\
Prod. of gems (incl. diamonds, dummy) &	.08 &	\\
&	\raisebox{.7ex}[0pt]{  (.26)} &	\\
Oil production (dummy) &	.42 &	\\
&	\raisebox{.7ex}[0pt]{  (.35)} &	\\
Oil more than 1/3 of exports (dummy) &	&	.80$^{**}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.38)} \\
Oil rents per capita &	&	-.0003 \\
&	&	\raisebox{.7ex}[0pt]{  (.0002)} \\
Const. &	-10.08$^{***}$ &	-10.06$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (3.32)} &	\raisebox{.7ex}[0pt]{  (3.54)} \\
\hline
Obs. &	5061 &	4650 \\
$ \chi^2$ statistic &	102.39 &	125.91 \\
Pseudo \(R^{2}\) &0.1 &0.1 \\
\hline\hline    
\multicolumn{3}{l}{\footnotesize Clustered standard errors in parentheses. * \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\
\end{tabular*}%    
\end{table}


\begin{table}
\scalebox{0.85}{
\caption{Additional Robustness Tests for Onset of Conflict, Military Personnel and Expenditures}

\label{table:military}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lcc}
& \multicolumn{1}{c}{Mil. Pers. \& Exp.} &\multicolumn{1}{c}{Energy, Iron \& Steel} \\
\cline{1-3}
& \multicolumn{1}{c}{(1)\mbox{\ }} &\multicolumn{1}{c}{(2)} \\
\hline
Urban Concentration &	2.34$^{***}$ &	1.72$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.77)} &	\raisebox{.7ex}[0pt]{  (.81)} \\
Urbanization &	-1.53$^{**}$ &	-.93 \\
&	\raisebox{.7ex}[0pt]{  (.76)} &	\raisebox{.7ex}[0pt]{  (.90)} \\
X-Polity &	.02 &	.03 \\
&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
Pop. size &	.26$^{**}$ &	.58$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.13)} &	\raisebox{.7ex}[0pt]{  (.16)} \\
Pop. aged 0-24 (\%) &	.06$^{***}$ &	.06$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.02)} \\
GDP per capita (log) &	-.18 &	-.11 \\
&	\raisebox{.7ex}[0pt]{  (.22)} &	\raisebox{.7ex}[0pt]{  (.23)} \\
Discrimination &	.57 &	.79 \\
&	\raisebox{.7ex}[0pt]{  (.53)} &	\raisebox{.7ex}[0pt]{  (.61)} \\
Mountainous terrain (\% log)  &	.26$^{***}$ &	.22$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.08)} &	\raisebox{.7ex}[0pt]{  (.08)} \\
Territory size (mln sq km) &	.07 &	.06 \\
&	\raisebox{.7ex}[0pt]{  (.1)} &	\raisebox{.7ex}[0pt]{  (.1)} \\
Share of pop. in armed forces (log) &	-2.08 &	-15.89 \\
&	\raisebox{.7ex}[0pt]{  (13.93)} &	\raisebox{.7ex}[0pt]{  (14.42)} \\
Mil. expenditures per capita (log)  &	.37$^{***}$ &	.52$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.14)} &	\raisebox{.7ex}[0pt]{  (.14)} \\
Energy Consumption (log) &	&	-.14$^{**}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.06)} \\
Iron and Steel prod. (log) &	&	-.05$^{**}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.02)} \\
Const. &	-9.78$^{***}$ &	-12.73$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (2.90)} &	\raisebox{.7ex}[0pt]{  (3.25)} \\
Obs. &	5248 &	5215 \\
$ F$ statistic &	&	\\
$ \chi^2$ statistic &	163.96 &	147.25 \\
Pseudo \(R^{2}\) &0.12 &0.13 \\
\hline\hline
\multicolumn{3}{l}{\footnotesize Clustered standard errors in parentheses. * \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\ 
\end{tabular*}
}%
\end{table}

\begin{table}
\caption{Controlling for Economic Growth and Urbanization Growth}
\label{tab:growth}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lcc}	
& \multicolumn{1}{c}{Economic Growth} & \multicolumn{1}{c}{Pace of Urbanization}\\
\cline{1-3} & \multicolumn{1}{c}{(1)\mbox{\ }} &\multicolumn{1}{c}{(2)} \\
\hline
Urban Concentration &	2.43$^{***}$ &	2.42$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.74)} &	\raisebox{.7ex}[0pt]{  (.75)} \\
Urbanization &	-.30 &	-.28 \\
&	\raisebox{.7ex}[0pt]{  (.76)} &	\raisebox{.7ex}[0pt]{  (.77)} \\
Pace of Urbanization &	&	-9.29 \\
&	&	\raisebox{.7ex}[0pt]{  (29.40)} \\
X-Polity &	.007 &	.007 \\
&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
Population Size (log) &	.34$^{***}$ &	.34$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.12)} &	\raisebox{.7ex}[0pt]{  (.12)} \\
\% of pop. age 0-24 &	.06$^{**}$ &	.06$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.02)} \\
GDP per capita &	.03 &	.04 \\
&	\raisebox{.7ex}[0pt]{  (.18)} &	\raisebox{.7ex}[0pt]{  (.16)} \\
GDP per capita growth  &	-1.91 &	-1.89 \\
&	\raisebox{.7ex}[0pt]{  (1.21)} &	\raisebox{.7ex}[0pt]{  (1.20)} \\
Discrimination &	.009$^{*}$ &	.009$^{*}$ \\
&	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.005)} \\
Mountainous terrain (\% log) &	.19$^{**}$ &	.19$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.09)} &	\raisebox{.7ex}[0pt]{  (.09)} \\
Territory (mln sq km) &	.08 &	.08 \\
&	\raisebox{.7ex}[0pt]{  (.10)} &	\raisebox{.7ex}[0pt]{  (.10)} \\
Const. &	-11.21$^{***}$ &	-11.29$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (2.89)} &	\raisebox{.7ex}[0pt]{  (2.80)} \\
\hline
Obs. &	5365 &	5365 \\
$ \chi^2$ statistic &	131.01 &	131.05 \\
\(R^{2}\) &	0.1 & 0.1 \\
\hline\hline	
\multicolumn{3}{l}{\footnotesize Clustered standard errors in parentheses. * \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\
\end{tabular*}%	

\end{table}


\begin{table}
\scalebox{0.75}{
\centering
\caption{Additional Robustness Test for Onset, Alternative Measure of Urban Concentration (300,000 Cut-off)}
\label{table:300kcutoff}
\begin{tabular}{lllllll}
& \multicolumn{1}{c}{Bivariate}                    & \multicolumn{1}{c}{Base model}                    & \multicolumn{1}{c}{No lag}                        & \multicolumn{1}{c}{No clustered SE}              & \multicolumn{1}{c}{Decade FE}                    & \multicolumn{1}{c}{Region FE}                    \\ \cline{1-7} 
& \multicolumn{1}{c}{(1)}                          & \multicolumn{1}{c}{(2)}                           & \multicolumn{1}{c}{(3)}                           & \multicolumn{1}{c}{(4)}                           & \multicolumn{1}{c}{(5)}                           & \multicolumn{1}{c}{(6)}                           \\ \hline
Urban concentration         &	1.32$^{*}$ &	2.45$^{**}$ & 3.29$^{***}$	&	2.45$^{**}$ &	2.21$^{*}$ &	1.88$^{*}$ \\
&	\raisebox{.7ex}[0pt]{  (.76)} &	\raisebox{.7ex}[0pt]{  (1.05)} &(.94)	&	\raisebox{.7ex}[0pt]{  (1.01)} &	\raisebox{.7ex}[0pt]{  (1.19)} &	\raisebox{.7ex}[0pt]{  (1.08)} \\
Urbanization  &	&	-.19 &	-.18 &	-.19 &	-.79 &	.30 \\
&	&	\raisebox{.7ex}[0pt]{  (.69)} &	\raisebox{.7ex}[0pt]{  (.75)} &	\raisebox{.7ex}[0pt]{  (.82)} &	\raisebox{.7ex}[0pt]{  (.75)} &	\raisebox{.7ex}[0pt]{  (.75)} \\
X-Polity  &	&	.001 &	.001 &	.001 &	.006 &	.03 \\
&	&	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
Population  (log) &	&	.33$^{***}$ &	.42$^{***}$ &	.33$^{***}$ &	.26$^{**}$ &	.21$^{*}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.12)} &	\raisebox{.7ex}[0pt]{  (.13)} &	\raisebox{.7ex}[0pt]{  (.11)} &	\raisebox{.7ex}[0pt]{  (.12)} &	\raisebox{.7ex}[0pt]{  (.12)} \\
Pop. Age 0-24 (\%)    &	&	.06$^{***}$ &	.07$^{***}$ &	.06$^{***}$ &	.05$^{**}$ &	.08$^{***}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.02)} &	\raisebox{.7ex}[0pt]{  (.03)} \\
GDP Per capita  (log)  &	&	.04 &	.07 &	.04 &	.09 &	.03 \\
&	&	\raisebox{.7ex}[0pt]{  (.17)} &	\raisebox{.7ex}[0pt]{  (.18)} &	\raisebox{.7ex}[0pt]{  (.18)} &	\raisebox{.7ex}[0pt]{  (.19)} &	\raisebox{.7ex}[0pt]{  (.18)} \\
Discrimination  &	&	.008 &	.008 &	.008 &	.007 &	.007 \\
&	&	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.005)} &	\raisebox{.7ex}[0pt]{  (.005)} \\
Mountainous terrain (\% log) &	&	.17$^{**}$ &	.16$^{*}$ &	.17$^{*}$ &	.17$^{**}$ &	.21$^{**}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.08)} &	\raisebox{.7ex}[0pt]{  (.09)} &	\raisebox{.7ex}[0pt]{  (.09)} &	\raisebox{.7ex}[0pt]{  (.08)} &	\raisebox{.7ex}[0pt]{  (.10)} \\
Territory (mil sq km)  &	&	.09 &	.07 &	.09 &	.11 &	.14$^{**}$ \\
&	&	\raisebox{.7ex}[0pt]{  (.09)} &	\raisebox{.7ex}[0pt]{  (.10)} &	\raisebox{.7ex}[0pt]{  (.05)} &	\raisebox{.7ex}[0pt]{  (.08)} &	\raisebox{.7ex}[0pt]{  (.07)} \\
Const. &	-4.26$^{***}$ &	-11.23$^{***}$ &	-13.04$^{***}$ &	-11.23$^{***}$ &	-10.21$^{***}$ &	-24.90$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.16)} &	\raisebox{.7ex}[0pt]{  (2.97)} &	\raisebox{.7ex}[0pt]{  (2.99)} &	\raisebox{.7ex}[0pt]{  (2.51)} &	\raisebox{.7ex}[0pt]{  (3.02)} &	\raisebox{.7ex}[0pt]{  (3.25)} \\
\hline
Obs. &	6162 &	5406 &	5676 &	5406 &	5406 &	5406 \\
$ F$ statistic &	&	&	&	&	&	\\
$ \chi^2$ statistic &	2.74 &	109.79 &	98.35 &	91.57 &	115.1 &	299.68 \\
\(R^{2}\)   &	.003 &	.1 &	.1 &	.1 &	.1 &	.11 \\
\hline \hline
\multicolumn{6}{l}{\footnotesize Clustered standard errors in parentheses. * \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\
\end{tabular}
}
\end{table}




\begin{table}
\caption{Additional Robustness Test for Onset, Alternative Measure of Concentration: Primacy, or \% of Urban Population Living in the Largest City}
\label{tab:shareofurban}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lc}	
\cline{2-2}	
\hline	
Urban Concentration (primacy) 	2.37$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.98)} \\
Urbanization &	-.25 \\
&	\raisebox{.7ex}[0pt]{  (.70)} \\
Population Size (log) &	.37$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (.12)} \\
\% of pop. age 0-24 &	.06$^{**}$ \\
&	\raisebox{.7ex}[0pt]{  (.02)} \\
GDP per capita (log) &	.07 \\
&	\raisebox{.7ex}[0pt]{  (.18)} \\
Discrimination  &	.007 \\
&	\raisebox{.7ex}[0pt]{  (.005)} \\
Mountainous terrain (\% log) &	.16$^{*}$ \\
&	\raisebox{.7ex}[0pt]{  (.09)} \\
Territory (mln sq km)&	.11 \\
&	\raisebox{.7ex}[0pt]{  (.08)} \\
Const. &	-12.18$^{***}$ \\
&	\raisebox{.7ex}[0pt]{  (3.00)} \\
\hline
Obs. &	5406 \\
$ \chi^2$ statistic &	111.92 \\
\(R^{2}\)    &	0.1 \\
\hline\hline	
\end{tabular*}%	
\end{table}

\begin{table}
\caption{Additional Robustness Test for Onset, Controlling for Colonial Legacy and Political Institutions}
\label{tab:institutions}
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{l*{5}{c}}
\hline
                    &\multicolumn{1}{c}{Col. Past}&\multicolumn{1}{c}{Fmr British}&\multicolumn{1}{c}{Ex-Colonies}&\multicolumn{1}{c}{Settler Mortality}&\multicolumn{1}{c}{Dispersive}\\
                    &\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}\\
\hline
Urban Concentration  &        2.38\sym{***}&        2.37\sym{***}&        2.90\sym{***}&        2.31\sym{***}&       
   2.08\sym{**} \\
                    &      (0.73)         &      (0.83)         &      (0.73)         &      (0.86)         &      (0.82)         \\
Never Colonized    &       -0.34         &       -0.33         &                     &                     &         
              \\
                    &      (0.31)         &      (0.32)         &                     &                     &         
              \\
Former British Colony      &                     &        0.01         &                     &                     &         
              \\
                    &                     &      (0.35)         &                     &                     &         
              \\
Settler Mortality (log) &                     &                     &                     &        0.03         &        
               \\
                    &                     &                     &                     &      (0.16)         &         
              \\
Dispersive Powersharing &                     &                     &                     &                     &   
       0.28         \\
                    &                     &                     &                     &                     &      (0.22)         \\
Urbanization (\%)  &       -0.24         &       -0.24         &       -0.28         &        0.04         &       -0.40         \\
                    &      (0.70)         &      (0.77)         &      (0.70)         &      (0.90)         &      (0.71)         \\
X-Polity        &       -0.00         &       -0.00         &        0.01         &        0.03         &        0.04         \\
                    &      (0.03)         &      (0.03)         &      (0.03)         &      (0.03)         &      (0.05)         \\
Population Size &        0.34\sym{***}&        0.34\sym{***}&        0.51\sym{***}&        0.43\sym{***}&        0.14         \\
                    &      (0.12)         &      (0.13)         &      (0.11)         &      (0.14)         &      (0.11)         \\
\% of pop. age 0-24 &        0.05\sym{*}  &        0.05\sym{*}  &        0.05         &        0.04         &        
  0.07\sym{**} \\
                    &      (0.03)         &      (0.02)         &      (0.03)         &      (0.04)         &      (0.04)         \\
GDP per capita    &        0.05         &        0.05         &        0.10         &       -0.18         &       
  -0.12         \\
                    &      (0.18)         &      (0.19)         &      (0.20)         &      (0.20)         &      (0.15)         \\
Discrimination      &        0.01\sym{*}  &        0.01\sym{*}  &        0.01         &        0.01\sym{**} &        0
  .02\sym{***}\\
                    &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         \\
Mountainous terrain &        0.21\sym{**} &        0.21\sym{**} &        0.20\sym{**} &        0.27\sym{**} & 
         0.25\sym{***}\\
                    &      (0.09)         &      (0.09)         &      (0.09)         &      (0.12)         &      (0.09)         \\
Territory (mln sq km) &        0.08         &        0.08         &       -0.16         &       -0.05         &        0
  .16\sym{**} \\
                    &      (0.10)         &      (0.10)         &      (0.12)         &      (0.08)         &      (0.08)         \\
Constant            &      -10.77\sym{***}&      -10.76\sym{***}&      -12.98\sym{***}&      -10.05\sym{**} &       -9
  .18\sym{**} \\
                    &      (2.97)         &      (3.04)         &      (3.30)         &      (4.03)         &      (3.58)         \\
\hline
Observations        &        5403         &        5403         &        3729         &        3157         &        2
  963         \\
Pseudo \(R^{2}\)    &       0.101         &       0.101         &       0.086         &       0.102         &       0.
  141         \\
\hline\hline
\multicolumn{6}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{6}{l}{\footnotesize \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
}
\end{table}

\begin{sidewaystable}
\scalebox{0.75}{
\centering
\caption{Pairwise Correlations for Urban Concentration,  Colonial Legacies, and Institutions variables}
\label{corrgram}
\begin{tabular}{lllllllllllllll}
  & \rotatebox{45}{Conc.}  &  & &  &  & &  &  &  & & & &  &  \\
Conc. & 1  & \rotatebox{45}{Primacy}  &  &  &  &  &  &   &  &  & &  & &  \\
Primacy & \cellcolor[HTML]{3166FF}0.88* & 1 & \rotatebox{45}{Urbaniz.} & & & & & & & & & & &\\
Urbaniz. & 0.08  & -0.18 & 1 & \rotatebox{45}{No Col. Hist.} & & & & & & & & & &\\
No Col. Hist. & -0.23 & -0.32 & \cellcolor[HTML]{3166FF}0.40* & 1 & \rotatebox{45}{Brit Col.} & & & &  & & & & & \\
Brit. Col. & -0.02  & -0.06 & -0.12 & \cellcolor[HTML]{FD6864}-0.36* & 1 & \rotatebox{45}{Settler Mort.} & & & & & & & & \\
Settler Mort.& 0.28 & 0.36 & \cellcolor[HTML]{FD6864}-0.61* & -0.41 & -0.15 & 1 & \rotatebox{45}{X-Polity} & & & & & & & \\
X-Polity & -0.23 & -0.32 & \cellcolor[HTML]{3166FF}0.48*  & \cellcolor[HTML]{3166FF}0.39*  & 0.06 & \cellcolor[HTML]{FD6864}-0.65* & 1 & \rotatebox{45}{Pop. Size} & & & & & &\\
Pop.Size & \cellcolor[HTML]{FD6864}-0.50* & \cellcolor[HTML]{FD6864}-0.61* & -0.02 & 0.13  & 0.11 & -0.35 & 0.22 & 1 & \rotatebox{45}{Youth Pop.} & & & & &\\
Youth Pop. & 0.24 & \cellcolor[HTML]{3166FF}{\color[HTML]{000000} 0.36*} & \cellcolor[HTML]{FD6864}-0.67* & \cellcolor[HTML]{FD6864}-0.73* & 0.18 & \cellcolor[HTML]{3166FF}0.59*  & \cellcolor[HTML]{FD6864}-0.58* & -0.16 & 1 & \rotatebox{45}{GDP/capita}  & & & & \\
GDP/capita & -0.19  & -0.28& \cellcolor[HTML]{3166FF}0.90* & \cellcolor[HTML]{3166FF}0.54*  & -0.21 & \cellcolor[HTML]{FD6864}-0.68* & \cellcolor[HTML]{3166FF}0.63*  & 0.06 & \cellcolor[HTML]{FD6864}-0.74* & 1 & \rotatebox{45}{Discr.} & & & \\
Discr. & 0.13 & 0.20 & -0.16 & -0.12 & 0.20 & 0.02 & 0.04  & -0.11  & 0.21 & -0.20 & 1 & \rotatebox{45}{Mnt Terr.} & &\\
Mtn Terr. & -0.08 & -0.06 & -0.06 & -0.06 & -0.01 & -0.32 & 0.04 & 0.21 & 0.17 & 0.06 & 0.05 & 1 & \rotatebox{45}{Terr. Size} & \\
Terr. Size & -0.22 & \cellcolor[HTML]{FD6864}-0.36* & 0.14& -0.09 & 0.21 & -0.21 & 0.15 & \cellcolor[HTML]{3166FF}0.45* & -0.12 & 0.09 & -0.08 & 0.16 & 1 & \rotatebox{45}{Dispersive} \\
Dispersive & -0.21  & \cellcolor[HTML]{FD6864}-0.35* & \cellcolor[HTML]{3166FF}0.45* & 0.18 & 0.03 & -0.33 & \cellcolor[HTML]{3166FF}0.50*  & \cellcolor[HTML]{3166FF}0.36* & \cellcolor[HTML]{FD6864}-0.47* & \cellcolor[HTML]{3166FF}0.50* & -0.18 & 0.05 & \cellcolor[HTML]{3166FF}0.42* & 1  

\end{tabular}
}
\end{sidewaystable}


\begin{table}
\scalebox{0.8}{
\caption{Log-linear Models of Civil War Battle Deaths, robustness tests}
\label{tab:log-linear}
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{l*{6}{c}}
& Bivariate & Baseline & No lag      & Decade FE &Region FE            & RE                \\
\hline
&\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}\\
\hline
Urban Concentration &        1.76\sym{***}&        2.10\sym{**} &                     &        2.15\sym{**} &        2.28\sym{**} &        3.77\sym{***}\\
                    &      (0.42)         &      (1.02)         &                     &      (1.05)         &      (1.10)         &      (0.86)         \\
Urban Concentration (not lagged) &                     &                     &        1.93\sym{**} &                     &                     &                     \\
                    &                     &                     &      (0.75)         &                     &                     &                     \\
Urbanization (\%) &                     &       -1.13         &       -1.13         &       -1.37         &       -1.31         &       -1.09         \\
                    &                     &      (1.00)         &      (1.00)         &      (1.00)         &      (1.05)         &      (0.69)         \\
X-Polity          &                     &       -0.03         &       -0.03         &       -0.02         &       -0.03         &       -0.02         \\
                    &                     &      (0.03)         &      (0.03)         &      (0.03)         &      (0.03)         &      (0.02)         \\
Pop. size  &                     &       -0.03         &       -0.02         &       -0.06         &       -0.00         &        0.08         \\
                    &                     &      (0.10)         &      (0.10)         &      (0.11)         &      (0.11)         &      (0.12)         \\
Pop. aged 0-24 &                     &        0.02         &        0.02         &        0.01         &        0.02         &       -0.01         \\
                    &                     &      (0.02)         &      (0.02)         &      (0.02)         &      (0.03)         &      (0.02)         \\
GDP per capita (log)     &                     &       -0.13         &       -0.15         &       -0.14         &       -0.11         &       -0.18         \\
                    &                     &      (0.21)         &      (0.20)         &      (0.20)         &      (0.21)         &      (0.15)         \\
Discrimination    &                     &        0.00         &        0.00         &        0.00         &        0.00         &        0.00         \\
                    &                     &      (0.00)         &      (0.00)         &      (0.00)         &      (0.00)         &      (0.00)         \\
Mountainous terrain (\% log) &                     &        0.32\sym{***}&        0.33\sym{***}&        0.34\sym{***}&        0.31\sym{**} &        0.35\sym{***}\\
                    &                     &      (0.10)         &      (0.11)         &      (0.10)         &      (0.13)         &      (0.10)         \\
Territory size                     &                     &        0.17\sym{***}&        0.16\sym{***}&        0.17\sym{***}&        0.15\sym{***}&        0.13\sym{**} \\
                    &                     &      (0.03)         &      (0.03)         &      (0.04)         &      (0.03)         &      (0.06)         \\
Conflict spell duration &                     &        0.02         &        0.02         &        0.02\sym{*}  &        0.02\sym{*}  &        0.03\sym{***}\\
                    &                     &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         \\
Constant            &        5.28\sym{***}&        4.49         &        4.67\sym{*}  &        5.02\sym{*}  &        3.96         &        5.57\sym{**} \\
                    &      (0.08)         &      (2.87)         &      (2.74)         &      (2.78)         &      (3.22)         &      (2.26)         \\
Decade fixed effects       &        &       &                  &        Yes        &           &                     \\
Region fixed effects &           &              &                          &                &       Yes &       \\
Random  effects &         &          &             &                                &        &        Yes             \\
\hline
Observations        &        1100         &        1018         &        1037         &        1018         &        1018         &        1018         \\
\(R^{2}\)           &       0.016         &       0.160         &       0.160         &       0.182         &       0.162         &                     \\
\hline\hline
\multicolumn{7}{l}{\footnotesize Standard errors in parentheses. * \(p<0.10\),** \(p<0.05\), *** \(p<0.01\)}\\
\end{tabular}
}
}
\end{table}



\clearpage

\begin{table}
\caption{Random-Effects Negative Binomial Models of Civil War Battle Deaths, robustness tests}
\label{tab:nbreg}
\resizebox{\textwidth}{!}{%
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{l*{6}{c}}

&Bivariate & Baseline       & No lag                     &  Decade FE &Region FE        &Country FE          \\
\hline
&\multicolumn{1}{c}{(1)}&\multicolumn{1}{c}{(2)}&\multicolumn{1}{c}{(3)}&\multicolumn{1}{c}{(4)}&\multicolumn{1}{c}{(5)}&\multicolumn{1}{c}{(6)}\\

\hline
Urban Concentration &        1.09\sym{***}&        1.59\sym{***}&                     &        1.50\sym{***}&        1.83\sym{***}&        1.50\sym{***}\\
                    &      (0.25)         &      (0.34)         &                     &      (0.34)         &      (0.35)         &      (0.35)         \\
Urban Concentration (not lagged) &                     &                     &        1.45\sym{***}&                     &                     &                     \\
                    &                     &                     &      (0.34)         &                     &                     &                     \\
Urbanization (\%) &                     &       -0.07         &        0.23         &       -0.02         &       -0.14         &       -0.04         \\
                    &                     &      (0.27)         &      (0.26)         &      (0.28)         &      (0.28)         &      (0.28)         \\
X-Polity            &                     &        0.00         &       -0.00         &       -0.00         &       -0.00         &       -0.00         \\
                    &                     &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         \\
Pop. size &                     &        0.06         &        0.08\sym{**} &        0.08\sym{**} &        0.15\sym{***}&        0.08\sym{*}  \\
                    &                     &      (0.04)         &      (0.04)         &      (0.04)         &      (0.04)         &      (0.04)         \\
Pop. aged 0-24 (\%) &                     &       -0.00         &       -0.01         &       -0.01\sym{*}  &       -0.01         &       -0.01         \\
                    &                     &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         &      (0.01)         \\
GDP per capita  (log)            &                     &        0.07         &        0.01         &        0.03         &        0.09         &        0.08         \\
                    &                     &      (0.06)         &      (0.06)         &      (0.06)         &      (0.07)         &      (0.06)         \\
Discrimination    &                     &       -0.00         &       -0.00         &       -0.00         &       -0.00         &       -0.00\sym{*}  \\
                    &                     &      (0.00)         &      (0.00)         &      (0.00)         &      (0.00)         &      (0.00)         \\
Mountainous terrain (\% log) &                     &        0.02         &        0.04         &        0.03         &       -0.01         &       -0.02         \\
                    &                     &      (0.03)         &      (0.03)         &      (0.03)         &      (0.04)         &      (0.04)         \\
Territory size &                     &        0.00         &       -0.02         &       -0.02         &       -0.09\sym{***}&       -0.02         \\
                    &                     &      (0.02)         &      (0.02)         &      (0.02)         &      (0.03)         &      (0.02)         \\
Conflict spell duration &                     &        0.01\sym{***}&                     &                     &                     &        0.01\sym{**} \\
                    &                     &      (0.00)         &                     &                     &                     &      (0.00)         \\
\hline 
Decade fixed effects  &            &              &                     &       Yes &                     &                     \\
Region fixed effects &            &                 &             &                  &     Yes &                     \\
Country fixed effects &            &                 &             &                  &      &   Yes                  \\
\hline
Observations        &        1162         &        1029         &        1048         &        1029         &        1029         &        1016         \\
Wald $ \chi^2$ & 18.92 & 42.41 & 31.00 & 57.81 & 50.30 & 42.17 \\
\hline\hline
\multicolumn{7}{l}{\footnotesize Standard errors in parentheses. \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
}
}
\end{table}

\begin{table}
\scalebox{0.85}{
\caption{Hurdle Model (exponential) of Civil War Battle Deaths}
\label{tab:hurdle}
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\begin{tabular}{l*{1}{c}}
\hline
Outcome conditional on having cleared hurdle\\
\hline
Urban Concentration &        1.92\sym{***}\\
                    &      (0.49)         \\
Urbanization (\%)    &       -1.13\sym{**} \\
                    &      (0.45)         \\
X-Polity            &       -0.03\sym{**} \\
                    &      (0.02)         \\
Pop. Size (log)  &       -0.02         \\
                    &      (0.06)         \\
\% of pop. age 0-24 &        0.02\sym{*}  \\
                    &      (0.01)         \\
GDP per capita (log)        &       -0.14         \\
                    &      (0.10)         \\
Discrimination       &        0.00         \\
                    &      (0.00)         \\
Mountainous terrain (\% log) &        0.33\sym{***}\\
                    &      (0.05)         \\
Territory (mil sq km) &        0.17\sym{***}\\
                    &      (0.03)         \\
Conflict spell duration &        0.02\sym{***}\\
                    &      (0.01)         \\
Constant            &        4.46\sym{***}\\
                    &      (1.32)         \\                    
\hline \hline
Hurdle  (Selection) equation\\
\hline
Urban Concentration &        2.42\sym{***}\\
                    &      (0.22)         \\
Urbanization (\%)   &        0.47\sym{***}\\
                    &      (0.16)         \\
X-Polity            &        0.04\sym{***}\\
                    &      (0.01)         \\
Pop. Size (log) &        0.45\sym{***}\\
                    &      (0.02)         \\
\% of pop. age 0-24 &        0.05\sym{***}\\
                    &      (0.00)         \\
GDP per capita (log)        &       -0.08\sym{**} \\
                    &      (0.04)         \\
Discrimination       &        0.01\sym{***}\\
                    &      (0.00)         \\
Mountainous terrain (\% log) &        0.05\sym{***}\\
                    &      (0.02)         \\
Territory (mil sq km) &       -0.07\sym{***}\\
                    &      (0.01)         \\
Constant            &       -8.11\sym{***}\\
                    &      (0.49)         \\
\hline
Constant            &        0.56\sym{***}\\
                    &      (0.02)         \\
\hline
Observations        &        5564         \\
\(R^{2}\)           &          .07            \\
\hline\hline
\multicolumn{2}{l}{\footnotesize Standard errors in parentheses}\\
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
}
}
\end{table}


\begin{figure}[h!]
\begin{center}
\caption{Urban Concentration and Urbanization, 2010}
\includegraphics[width=7in]{scatterplotHHurbrate2010.pdf}
\label{scatterHHurban}
\end{center}
\end{figure}


\begin{table}[]
\centering
\caption{Technologies of Rebellion and Urban Concentration, Collapsed}
\label{technologiestablecoll}
\begin{tabular}{llll}
                                 & \multicolumn{2}{l}{\textbf{High Concentration}} &       \\
\textbf{Technology of Rebellion} & No                     & Yes                    & Total \\
\hline
Asymmetric    & 46                     & 25                     & 71    \\
      & 65\%                   & 35\%                   &       \\\\
Symmetric   & 18                     & 37                     & 55    \\
  & 33\%                   & 67\%                   &       \\
  \hline \hline
Total       & 64                     & 62                     & 126  \\
\hline
$ \chi^2$ statistic = 12.7 &  \textit{p} < 0.000 &\\
\end{tabular}
\end{table}
\clearpage 


\begin{figure}[h!]
\begin{center}
\caption{Technologies of Rebellion and Urban Concentration}
\includegraphics[width=7in]{technologyrebellion_coll.pdf}
\label{technologies}
\end{center}
\end{figure}

\clearpage 
\begin{table}[]
\centering
\caption{Technologies of Rebellion and Urban Concentration}
\label{technologiestable}
\begin{tabular}{llll}
                                 & \multicolumn{2}{l}{\textbf{High Concentration}} &       \\
\textbf{Technology of Rebellion} & No                     & Yes                    & Total \\
\hline
Conventional    & 12                     & 21                     & 33    \\
      & 36.4\%                   & 63.6\%                   &       \\\\
Irregular   & 41                     & 21                     & 62    \\
  & 66.1\%                   & 33.9\%                   &       \\\\
Symmetric Nonconventional   & 1                      & 14                     & 15    \\
    & 6.7\%                    & 93.3\%                   &       \\
    \hline \hline
Total       & 54                     & 56                     & 110  
\end{tabular}
\end{table}



\begin{figure}[h!]
\begin{center}
\caption{Technologies of Rebellion and Urban Concentration}
\includegraphics[width=7in]{technologyrebellion.pdf}
\label{technologies}
\end{center}
\end{figure}

\begin{table}
{
\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}
\caption{OLS Model of Change in Urban Concentration}
\label{OLSendogeneity}
\begin{tabular}{l*{1}{c}}
\hline
                    &\multicolumn{1}{c}{Urban Concentration, Annual Change \%)}\\
\hline
Incidence of intrastate conflict &       -0.39\sym{*}  \\
                    &      (0.20)         \\
Incidence 1-year lag &        0.21         \\
                    &      (0.15)         \\
Incidence 2-year lag &        0.03         \\
                    &      (0.07)         \\
Lagged DV   &        0.87\sym{***}\\
                    &      (0.02)         \\
Population Growth (\% change)          &       -6.63\sym{*}  \\
                    &      (3.46)         \\
Urban Concentration (absolute) &       -0.89\sym{**} \\
                    &      (0.43)         \\
Population Size (logged)           &       -0.07\sym{**} \\
                    &      (0.03)         \\
Level of Urbanization (\%)   &       -0.27         \\
                    &      (0.18)         \\
GDP per capita, annual change \%  &        0.07\sym{**} \\
                    &      (0.03)         \\
Constant            &        0.99\sym{**} \\
                    &      (0.40)         \\
\hline
Observations        &        5927         \\
\(R^{2}\)           &       0.798         \\
\hline\hline
\multicolumn{2}{l}{\footnotesize Clustered Standard errors in parentheses}\\
\multicolumn{2}{l}{\footnotesize \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\)}\\
\end{tabular}
}
\end{table}
